SoroushVahidi/scidocs huggingface dataset:
Material classification and semantic segmentation of railway track images with deep convolutional neural networks
(2015)

The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN). We show that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classification accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.
A conversational intelligent tutoring system to automatically predict learning styles
(2012)

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.
Magic Quadrant for Data Quality Tools
(2011)

Extensive data on functional capabilities, customer base demographics, financial status, pricing and other quantitative attributes gained via a requestfor-information process engaging vendors in this market. Interactive briefings in which vendors provided Gartner with updates on their strategy, market positioning, recent key developments and product road map. A Web-based survey of reference customers provided by each vendor, which captured data on usage patterns, levels of satisfaction with major product functionality categories, various nontechnology-related vendor attributes (such as pricing, product support and overall service delivery), and more. In total, 333 organizations across all major world regions provided input on their experiences with vendors and tools in this manner. Feedback about tools and vendors captured during conversations with users of Gartner's client inquiry service. Market share and revenue growth estimates developed by Gartner's Technology and Service Provider research unit.
A study of near-field direct antenna modulation systems using convex optimization
(2010)

This paper studies the constellation diagram design for a class of communication systems known as near-field direct antenna modulation (NFDAM) systems. The modulation is carried out in a NFDAM system by means of a control unit that switches among a number of pre-designed passive controllers such that each controller generates a desired voltage signal at the far field. To find an optimal number of signals that can be transmitted and demodulated reliably in a NFDAM system, the coverage area of the signal at the far field should be identified. It is shown that this coverage area is a planar convex region in general and simply a circle in the case when no constraints are imposed on the input impedance of the antenna and the voltage received at the far field. A convex optimization method is then proposed to find a polygon that is able to approximate the coverage area of the signal constellation diagram satisfactorily. A similar analysis is provided for the identification of the coverage area of the antenna input impedance, which is beneficial for designing an energy-efficient NFDAM system.
BFS and Coloring-Based Parallel Algorithms for Strongly Connected Components and Related Problems
(2014)

Finding the strongly connected components (SCCs) of a directed graph is a fundamental graph-theoretic problem. Tarjan's algorithm is an efficient serial algorithm to find SCCs, but relies on the hard-to-parallelize depth-first search (DFS). We observe that implementations of several parallel SCC detection algorithms show poor parallel performance on modern multicore platforms and large-scale networks. This paper introduces the Multistep method, a new approach that avoids work inefficiencies seen in prior SCC approaches. It does not rely on DFS, but instead uses a combination of breadth-first search (BFS) and a parallel graph coloring routine. We show that the Multistep method scales well on several real-world graphs, with performance fairly independent of topological properties such as the size of the largest SCC and the total number of SCCs. On a 16-core Intel Xeon platform, our algorithm achieves a 20X speedup over the serial approach on a 2 billion edge graph, fully decomposing it in under two seconds. For our collection of test networks, we observe that the Multistep method is 1.92X faster (mean speedup) than the state-of-the-art Hong et al. SCC method. In addition, we modify the Multistep method to find connected and weakly connected components, as well as introduce a novel algorithm for determining articulation vertices of biconnected components. These approaches all utilize the same underlying BFS and coloring routines.
Evaluation of communication technologies for IEC 61850 based distribution automation system with distributed energy resources
(2009)

This paper presents the study of different communication systems between IEC 61850 based distribution substation and distributed energy resources (DERs). Communication networks have been simulated for a typical distribution automation system (DAS) with DERs using OPNET software. The simulation study shows the performance of wired and wireless communication systems for different messages, such as GOOSE and measured (metered) values between DAS and DERs. A laboratory set-up has been implemented using commercial relay and communication devices for evaluating the performance of GOOSE messages, using wired and wireless physical medium. Finally, simulation and laboratory results are discussed in detail.
A clinical study of Noonan syndrome.
(1992)

Clinical details are presented on 151 individuals with Noonan syndrome (83 males and 68 females, mean age 12.6 years). Polyhydramnios complicated 33% of affected pregnancies. The commonest cardiac lesions were pulmonary stenosis (62%), and hypertrophic cardiomyopathy (20%), with a normal echocardiogram present in only 12.5% of all cases. Significant feeding difficulties during infancy were present in 76% of the group. Although the children were short (50% with a height less than 3rd centile), and underweight (43% with a weight less than 3rd centile), the mean head circumference of the group was on the 50th centile. Motor milestone delay was usual, the cohort having a mean age of sitting unsupported of 10 months and walking of 21 months. Abnormal vision (94%) and hearing (40%) were frequent findings, but 89% of the group were attending normal primary or secondary schools. Other associations included undescended testicles (77%), hepatosplenomegaly (50%), and evidence of abnormal bleeding (56%). The mean age at diagnosis of Noonan syndrome in this group was 9.0 years. Earlier diagnosis of this common condition would aid both clinical management and genetic counselling.
Superneurons: dynamic GPU memory management for training deep neural networks
(2018)

Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c.
BRAVO: Balanced Reliability-Aware Voltage Optimization
(2017)

Defining a processor micro-architecture for a targeted productspace involves multi-dimensional optimization across performance, power and reliability axes. A key decision in sucha definition process is the circuit-and technology-driven parameterof the nominal (voltage, frequency) operating point. This is a challenging task, since optimizing individually orpair-wise amongst these metrics usually results in a designthat falls short of the specification in at least one of the threedimensions. Aided by academic research, industry has nowadopted early-stage definition methodologies that considerboth energy-and performance-related metrics. Reliabilityrelatedenhancements, on the other hand, tend to get factoredin via a separate thread of activity. This task is typically pursuedwithout thorough pre-silicon quantifications of the energyor even the performance cost. In the late-CMOS designera, reliability needs to move from a post-silicon afterthoughtor validation-only effort to a pre-silicon definitionprocess. In this paper, we present BRAVO, a methodologyfor such reliability-aware design space exploration. BRAVOis supported by a multi-core simulation framework that integratesperformance, power and reliability modeling capability. Errors induced by both soft and hard fault incidence arecaptured within the reliability models. We introduce the notionof the Balanced Reliability Metric (BRM), that we useto evaluate overall reliability of the processor across soft andhard error incidences. We demonstrate up to 79% improvementin reliability in terms of this metric, for only a 6% dropin overall energy efficiency over design points that maximizeenergy efficiency. We also demonstrate several real-life usecaseapplications of BRAVO in an industrial setting.
Ensemble Methods for Multi-label Classification
(2014)

Ensemble methods have been shown to be an effectiv e tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the en semble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of rando mly choosing subsets, we select the minimum required su bsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correla tions. Construction of the cover is achieved by form ulating the subset selection as a minimum set covering prob lem (SCP) and solving it by using approximation algo rithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may b e applied to the classification of any given multi-la bel dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. Fir st, we introduce SCP as a general framework for con structing label covers while allowing the user to incorpo rate cover construction constraints. We demonstrate he effectiveness of this framework by proposing two co nstruction constraints whose enforcement produces c overs that improve the prediction performance of ran dom selection. Second, we provide theoretical bound s that quantify the probabilities of random selection t produce covers that meet the proposed construct ion riteria. The experimental results indicate that the p roposed methods improve multi-label classification accuracy and stability compared with the RAKEL algorithm a nd to other state-of-the-art algorithms.
Sensor networks for medical care
(2005)

Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and
Digital Forensic Analysis of SIM Cards
(2016)

Smart cards are fundamental technology in modern life. It is embedded in numerous devices such as GPS devices, ATM cards, Mobile SIM cards and many others. Mobile devices became the evolution of technology. It becomes smaller, faster and supports large storage capabilities. Digital forensics of mobile devices that maybe found in crime scene is becoming inevitable. The purpose of this research is to address the SIM cards digital forensics analysis. It presents sound forensic methodology and process of SIM cards forensic examination. In particular, the main aim of the research is to answer the following research questions: (1) what forensic evidence could be extracted from a SIM card and (2) what are limitations that may hinder a forensic
Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems
(2017)

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.
A switching ringing suppression scheme of SiC MOSFET by Active Gate Drive
(2016)

This paper proposes an Active Gate Drive (AGD) to reduce unwanted switching ringing of Silicon Carbide (SiC) MOSFET module with a rating of 120 A and 1200 V. While SiC MOSFET can be operated under high switching frequency and high temperature with very low power losses, one of the key challenges for SiC MOSFET is the electromagnetic interference (EMI) caused by steep switching transients and continuous switching ringing. Compared to Si MOSFET, the higher rate of SiC MOSFET drain current variation introduces worse EMI problems. To reduce EMI generated from the switching ringing, this paper investigates the causes of switching ringing by considering the combined impact of parasitic inductances, capacitances, and low circuit loop resistance. In addition, accurate mathematical expressions are established to explain the ringing behavior and quantitative analysis is carried out to investigate the relationship between the switching transient and gate drive voltage. Thereafter, an AGD method for mitigating SiC MOSFET switching ringing is presented. Substantially reduced switching ringing can be observed from circuit simulations. As a result, the EMI generation is mitigated.
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
(2016)

Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications. Existing work in this emerging field was largely specific to the domain of image classification, since the highentropy of images can be conveniently manipulated without changing the images’ overall visual appearance. Yet, it remains unclear how such attacks translate to more securitysensitive applications such as malware detection–which may pose significant challenges in sample generation and arguably grave consequences for failure. In this paper, we show how to construct highly-effective adversarial sample crafting attacks for neural networks used as malware classifiers. The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain: (i) continuous, differentiable input domains are replaced by discrete, often binary inputs; and (ii) the loose condition of leaving visual appearance unchanged is replaced by requiring equivalent functional behavior. We demonstrate the feasibility of these attacks on many different instances of malware classifiers that we trained using the DREBIN Android malware data set. We furthermore evaluate to which extent potential defensive mechanisms against adversarial crafting can be leveraged to the setting of malware classification. While feature reduction did not prove to have a positive impact, distillation and re-training on adversarially crafted samples show promising results.
Lung nodules diagnosis based on evolutionary convolutional neural network
(2017)

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.
Crop disease classification using texture analysis
(2016)

With the Agriculture Sector being the backbone of not only a large number of industries but also society as a whole, there is a rising need to grow good quality crops which in turn will give a high yield. For this to happen, it is crucial to monitor the crops throughout their growth period. In this paper, Image processing is used to detect and classify sunflower crop diseases based on the image of their leaf. The images are taken through a high resolution digital camera and after preprocessing, are subjected to k-means clustering to get the diseased part of the leaf. These are then run through the various machine learning algorithms and classified based on their color and texture features. A comparison based on accuracy between various machine learning algorithms is done namely K-Nearest Neighbors, Multi-Class Support Vector Machine, Naive Bayes and Multinomial Logistic Regression to achieve maximum accuracy. The implementation has been done using MATLAB.
Practical Private Set Intersection Protocols with Linear Complexity
(2010)

The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.